如何在tensorflow slim中使用批处理标准化？

import tensorflow as tf
import tensorflow.contrib.slim as slim

inputs = tf.placeholder(tf.float32, [None, input_size], name='inputs')

net = slim.fully_connected(inputs, num_units, activation_fn=tf.nn.relu, scope='fc1')

net = slim.batch_norm(net, is_training=is_training, scope='batch_norm')

net = slim.fully_connected(net, num_units, activation_fn=tf.nn.relu, scope='fc2')

outputs = slim.fully_connected(net, num_classes, activation_fn=None, scope='output')

loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=outputs))
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for epoch in range(num_epochs):
        # 执行训练步骤
        sess.run(optimizer, feed_dict={inputs: train_data, labels: train_labels, is_training: True})